# Measurement Simplification in ρ-POMDP with Performance Guarantees

arXiv: https://arxiv.org/abs/2309.10701 · HTML: https://mlsanity.com/paper/2309.10701/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [Online algorithms for POMDPs with continuous state, action, and observation spaces](https://mlsanity.com/paper/1709.06196/) (2017)
- [Online Informative Path Planning for Active Classification Using UAVs](https://mlsanity.com/paper/1609.08446/) (2016)
- [Simplified decision making in the belief space using belief sparsification](https://mlsanity.com/paper/1909.00885/) (2019)
- [Adaptive Information Belief Space Planning](https://mlsanity.com/paper/2201.05673/) (2022)
- [General Purpose Incremental Covariance Update and Efficient Belief Space Planning via Factor-Graph Propagation Action Tree](https://mlsanity.com/paper/1906.02249/) (2019)
- [Designing Sparse Reliable Pose-Graph SLAM: A Graph-Theoretic Approach](https://mlsanity.com/paper/1611.00889/) (2016)

## Built on by (most cited first, 1 of 1)

- [Certified World Models as Sensing Clocks: Drift-Aware Deadlines for Active Perception](https://mlsanity.com/paper/2607.01537/) (2026)

## Abstract (arXiv)

Decision making under uncertainty is at the heart of any autonomous system acting with imperfect information. The cost of solving the decision making problem is exponential in the action and observation spaces, thus rendering it unfeasible for many online systems. This paper introduces a novel approach to efficient decision-making, by partitioning the high-dimensional observation space. Using the partitioned observation space, we formulate analytical bounds on the expected information-theoretic reward, for general belief distributions. These bounds are then used to plan efficiently while keeping performance guarantees. We show that the bounds are adaptive, computationally efficient, and that they converge to the original solution. We extend the partitioning paradigm and present a hierarchy of partitioned spaces that allows greater efficiency in planning. We then propose a specific variant of these bounds for Gaussian beliefs and show a theoretical performance improvement of at least a factor of 4. Finally, we compare our novel method to other state of the art algorithms in active SLAM scenarios, in simulation and in real experiments. In both cases we show a significant speed-up in planning with performance guarantees.